A Review on Optimization Algorithms for Feature Selection

Feature selection takes a vital role in image processing, data mining and clustering. Feature selection is an extremely important and familiar procedure which takes place before the classification process. Hence finding optimal feature subset for classification task is very important. However, the process of choosing the pertinent data or feature is a major challenging issue. Since feature selection needs to be accomplished to eradicate computational complexity of any image processing system and helps to classify the datasets efficiently. Therefore, this paper reviews various optimization algorithms used for novel feature selection. This study illustrates the latest available metaheuristic methods for optimal solution findings which consistently improves classification accuracy and reduces computational cost. Also, this paper gives different aspects of optimization algorithm. Here most recently used optimization algorithms and feature selection methods are discussed to enlighten the researchers in the field of feature selection and optimizing methods.